272 lines
9.3 KiB
Python
272 lines
9.3 KiB
Python
#!/usr/bin/env python3
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"""
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测试JSON-RPC工作流进度反馈
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Test JSON-RPC Workflow Progress Feedback
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"""
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import json
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import requests
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import time
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import threading
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import websocket
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from typing import Dict, Any
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class JSONRPCWorkflowTester:
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"""JSON-RPC工作流测试器"""
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def __init__(self, server_url: str = "http://localhost:8081"):
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self.server_url = server_url
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self.request_id = 0
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self.progress_updates = []
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def _call_method(self, method: str, params: Dict[str, Any]) -> Dict[str, Any]:
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"""调用JSON-RPC方法"""
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self.request_id += 1
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payload = {
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"jsonrpc": "2.0",
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"method": method,
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"params": params,
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"id": self.request_id
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}
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print(f"📤 发送请求 (ID: {self.request_id}): {method}")
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print(f" 参数: {json.dumps(params, indent=2, ensure_ascii=False)}")
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try:
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response = requests.post(
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self.server_url,
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json=payload,
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headers={'Content-Type': 'application/json'},
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timeout=120 # 增加超时时间
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)
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if response.status_code == 200:
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result = response.json()
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print(f"📥 收到响应 (ID: {self.request_id}): {response.status_code}")
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return result
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else:
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print(f"❌ HTTP错误: {response.status_code}")
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return {
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"error": {
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"code": response.status_code,
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"message": f"HTTP Error: {response.text}"
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}
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}
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except requests.exceptions.RequestException as e:
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print(f"❌ 请求失败: {str(e)}")
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return {
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"error": {
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"code": -1,
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"message": f"Request failed: {str(e)}"
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}
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}
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def test_basic_detection(self):
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"""测试基础场景检测"""
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print("\n" + "="*60)
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print("🎯 测试基础场景检测")
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print("="*60)
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params = {
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"video_path": "assets/1/1752032011698.mp4",
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"detector_type": "content",
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"threshold": 15.0,
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"min_scene_length": 1.0
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}
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start_time = time.time()
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result = self._call_method("scene.detect", params)
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end_time = time.time()
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if "error" in result:
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print(f"❌ 检测失败: {result['error']}")
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return False
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detection_result = result.get("result", {})
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if detection_result.get("success"):
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print(f"✅ 基础检测成功!")
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print(f" 场景数量: {detection_result['total_scenes']}")
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print(f" 检测时间: {detection_result['detection_time']:.2f}秒")
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print(f" API调用时间: {end_time - start_time:.2f}秒")
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return True
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else:
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print(f"❌ 检测失败: {detection_result.get('error', '未知错误')}")
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return False
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def test_workflow_detection(self):
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"""测试工作流场景检测"""
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print("\n" + "="*60)
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print("🔄 测试LangGraph工作流检测")
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print("="*60)
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params = {
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"video_path": "assets/1/1752032011698.mp4",
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"detector_type": "content",
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"threshold": 15.0,
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"min_scene_length": 1.0,
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"enable_ai_analysis": False # 禁用AI分析避免API密钥问题
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}
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start_time = time.time()
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result = self._call_method("scene.detect_workflow", params)
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end_time = time.time()
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if "error" in result:
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print(f"❌ 工作流检测失败: {result['error']}")
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return False
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workflow_result = result.get("result", {})
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detection_result = workflow_result.get("detection_result", {})
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if detection_result.get("success"):
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print(f"✅ 工作流检测成功!")
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print(f" 工作流状态: {workflow_result.get('workflow_state')}")
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print(f" 场景数量: {detection_result['total_scenes']}")
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print(f" 检测时间: {detection_result['detection_time']:.2f}秒")
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print(f" API调用时间: {end_time - start_time:.2f}秒")
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# 显示视频信息
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video_info = workflow_result.get("video_info", {})
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if video_info:
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print(f" 视频信息: {video_info.get('resolution')}, {video_info.get('fps'):.2f}fps")
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# 显示AI分析结果
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ai_analysis = workflow_result.get("ai_analysis")
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if ai_analysis:
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print(f" AI分析: {ai_analysis[:100]}...")
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return True
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else:
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print(f"❌ 工作流检测失败: {detection_result.get('error', '未知错误')}")
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return False
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def test_video_info(self):
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"""测试视频信息获取"""
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print("\n" + "="*60)
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print("📊 测试视频信息获取")
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print("="*60)
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params = {
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"video_path": "assets/1/1752032011698.mp4"
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}
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start_time = time.time()
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result = self._call_method("scene.get_video_info", params)
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end_time = time.time()
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if "error" in result:
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print(f"❌ 获取失败: {result['error']}")
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return False
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info_result = result.get("result", {})
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if info_result.get("success"):
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info = info_result.get("info", {})
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print(f"✅ 信息获取成功!")
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print(f" 文件名: {info.get('filename')}")
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print(f" 分辨率: {info.get('resolution')}")
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print(f" 帧率: {info.get('fps'):.2f} fps")
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print(f" 时长: {info.get('duration'):.2f}秒")
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print(f" 文件大小: {info.get('file_size'):,} 字节")
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print(f" API调用时间: {end_time - start_time:.2f}秒")
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return True
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else:
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print(f"❌ 获取失败: {info_result.get('error', '未知错误')}")
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return False
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def test_error_handling(self):
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"""测试错误处理"""
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print("\n" + "="*60)
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print("🚨 测试错误处理")
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print("="*60)
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# 测试不存在的文件
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params = {
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"video_path": "nonexistent_video.mp4",
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"threshold": 15.0
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}
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result = self._call_method("scene.detect", params)
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if "error" in result:
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print(f"✅ 正确处理了请求错误: {result['error']['message']}")
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else:
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detection_result = result.get("result", {})
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if not detection_result.get("success"):
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print(f"✅ 正确处理了应用错误: {detection_result.get('error')}")
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else:
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print(f"❌ 应该返回错误，但返回了成功结果")
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return False
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# 测试无效的方法
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result = self._call_method("scene.invalid_method", {})
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if "error" in result and result["error"]["code"] == -32601:
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print(f"✅ 正确处理了方法不存在错误")
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else:
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print(f"❌ 没有正确处理方法不存在错误")
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return False
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return True
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def run_all_tests(self):
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"""运行所有测试"""
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print("🚀 JSON-RPC工作流测试开始")
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print("="*60)
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# 检查服务器连接
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try:
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test_result = self._call_method("scene.get_video_info", {"video_path": "test.mp4"})
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if "error" in test_result and "Request failed" in str(test_result["error"]):
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print("❌ 无法连接到JSON-RPC服务器")
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print("💡 请先启动服务器: python3 -m python_core.cli jsonrpc start --port 8081")
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return False
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except Exception as e:
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print(f"❌ 连接测试失败: {e}")
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return False
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print("✅ 服务器连接正常")
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# 运行测试
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tests = [
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("视频信息获取", self.test_video_info),
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("基础场景检测", self.test_basic_detection),
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("工作流场景检测", self.test_workflow_detection),
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("错误处理", self.test_error_handling),
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]
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passed = 0
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total = len(tests)
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for test_name, test_func in tests:
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try:
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if test_func():
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passed += 1
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print(f"✅ {test_name} - 通过")
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else:
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print(f"❌ {test_name} - 失败")
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except Exception as e:
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print(f"❌ {test_name} - 异常: {e}")
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print("\n" + "="*60)
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print(f"🎉 测试完成: {passed}/{total} 通过")
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print("="*60)
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if passed == total:
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print("🎊 所有测试都通过了！JSON-RPC工作流功能正常")
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else:
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print("⚠️ 部分测试失败，请检查服务器和配置")
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return passed == total
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def main():
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"""主函数"""
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tester = JSONRPCWorkflowTester("http://localhost:8081")
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tester.run_all_tests()
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if __name__ == "__main__":
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main()
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